Recent Advances in Electrical & Electronic Engineering - Volume 14, Issue 5, 2021

Background: Microgrid is an effective integration mode of distributed generation (DG). It can eliminate the negative effect caused by the intermittent DG and consume the energy locally. Methods: For a public distribution network, a microgrid performs as a controllable and flexible unit. However, when the public distribution network has the demand of flexible regulation, there is no reasonable quantitative indexes to evaluate the flexibility of microgrid for dispatching decision. For this problem, the operational flexibility indexes of a microgrid are defined in this paper. Results: The indexes are quantified according to the maximum energy and active power regulation margin of each flexible equipment in the microgrid. The operational flexibility of microgrid is closely related to the scheduling strategy. Therefore, the corresponding dispatching model of a microgrid is also provided in this paper. Conclusion: A case study shows how to calculate and use these flexibility indexes in an operation cycle of a microgrid system with energy storage (ES) facility and demonstrates the availability and practicability of the evaluation indexes and the method.

Background: Correct classifying of analog circuit faults is helpful in the health management of the circuit. It is difficult to be implemented because of the lack of proper feature extraction methods and accurate fault diagnosis models. Objective: T-SNE based core components extraction method and PSO-ELM-based fault diagnosis model are presented to improve the diagnostic accuracy of analog circuit fault diagnosis. Methods: Firstly, circuit output signals are collected, and they are transformed to wavelet coefficients. Then, the high-dimensional wavelet coefficients are processed by t-SNE to generate lowdimensional core components as features. The Extreme Learning Machine (ELM) based diagnosing model is constructed by using the features, and the key parameters of ELM are optimized by using Particle Swarm Optimization (PSO) algorithm. Finally, the constructed PSO-ELM diagnosis model is employed to identify different analog circuit faults. Results: Leapfrog filter circuit and three-phase bridge circuit fault diagnosis experiments are implemented to demonstrate the proposed t-SNE based features extraction method and PSO-ELM based fault diagnosis model. Also, comparisons are performed to verify the high performance of proposed fault diagnosis methods. Conclusion: The proposed t-SNE based core components extraction method and PSO-ELM diagnosis model are effective to improve the fault diagnosis accuracy of the analog circuit.

Background: Lightning electromagnetic pulse (LEMP) and high-altitude electromagnetic pulse (HEMP) are widely described by three physical parameters (rise time tr, full-width at halfmaximum pulse width tw, and maximum electric field strength E0). These pulse shapes are often given by a double exponential form concerning four mathematical parameters, namely α, β, k and Ep. Objective: The transformation from physical parameters into mathematical parameters is necessary in waveform simulation and is traditionally accomplished by linear fit functions regarding the two groups of parameters. However, traditional methods commonly rely on data analysis and calculation. In order to obtain the mathematical parameters more concisely and clearly. Methods: In this paper, a numerical method to calculate the mathematical parameters by solving nonlinear equations with three key constraints is proposed. Firstly, we establish the nonlinear system of equations regarding four variables, namely t1, t2, α and β. Then, three constraints are given to converge the solutions of the equations. Lastly, select the minimal value of the convergent solution of each equation. Results: Comparing the solutions obtained by our proposed method to the iterated ones, the overall relative error is less than 2×10^-8. Conclusion: The results show that our proposed method not only simplifies the transformation from physical parameters to mathematical parameters, but also keeps the solutions high accurate.

Background: Phasor Measurement Unit (PMU) Data Manipulation Attacks (PDMA) can change the state estimates of power systems and cause significant damage to the smart grid. So it is vital to research a method to detect it. Objective: In this paper, we propose a detection mechanism and model for PDMA. Method: Firstly, the distribution's characteristics of Phasor Data Concentrator (PDC) and PMU are analyzed, and we use these characteristics to detect a PDMA detection mechanism that could help us reduce the number of detection samples. Secondly, we use the Sliced Recurrent Neural Network (SRNN) to extract the time series data's temporal characteristics of PMU data. Thirdly, based on the temporal characteristics, the Convolutional Neural Networks (CNN) and Attention mechanisms are used to extract the spatial features of these data. Finally, we sent the spatial features to the Fully Layer and used the softmax function to classify. Results: The proposed SRCAM in this paper has two advantages. One is that it implements the parallel computation on data by using the segmentation concept of SRNN, which reduces the computation time. The other is that using the Attention mechanism on CNN can make the spatial features more prominent. At the end of the paper, we do many comparative experiments between SRCAM and other models, such as some algorithms of Machine learning and soft computing. We take IEEE node data as experimental data and TensorFlow as an experimental platform. Experimental results show that the SRCAM model has an excellent performance of the detection of PDMA with high precision and accuracy. Conclusion: The superiority of SRCAM is theoretically and experimentally proved in this paper. As we expected, SRCAM showed great results in the application of PDMA detection.

Background: In the power Internet of Things (IoT), power consumption data faces the risk of privacy leakage. Traditional privacy-preserving schemes cannot ensure data privacy on the system, as the secret key pairs shall be shared between all the interior nodes once leaked. In addition, the general schemes only support summation algorithms, resulting in a lack of extensibility. Objective: To preserve the privacy of power consumption data, ensure the privacy of secret keys, and support multiple data processing methods we propose an improved power consumption data privacypreserving scheme. Method: Firstly, we have established a power IoT architecture based on edge computing. Then the data is encrypted with the multi-key fully homomorphic algorithm to realize the operation of ciphertext, without the restrictions of calculation type. Through the improved decryption algorithm, ciphertext that can be separately decrypted in cloud nodes is generated, which contributes to reducing communication costs and preventing data leakage. Results: The experimental results show that our scheme is more efficient than traditional schemes in privacy preservation. According to the variance calculation result, the proposed scheme has reached the application standard in terms of computational cost and is feasible for practical operation. Discussion: In the future, we plan to adopt a secure multi-party computation based scheme so that data can be managed locally with homomorphic encryption, so as to ensure data privacy. Conclusion: A privacy-preserving scheme based on improved multi-key fully homomorphic encryption is proposed for the power consumption data, and the experimental results demonstrate the effectiveness and advantage of the proposed scheme.

Background: The traditional state-based non-intrusive load monitoring method mainly deploys the aggregate load as the characteristic to identify the states of every electrical appliance. Each identification is relatively independent, and there is no correlation between the identification results. Objective: This paper combines the event detection results with the state-based non-intrusive load identification algorithm to improve accuracy. Methods: Firstly, the load recognition model based on an artificial neural network is constructed, and the state-based recognition results are obtained. An event recognition and detection model is then built to identify electrical state transitions, that is, the current moment based on the event recognition results obtained from the previous moment. Finally, a reasonable decision method is constructed to determine the identification result of the electrical states. Results: Experimental results on the public data set REDD show that in the Long Short-Term Memory (LSTM) fusion model, the macro-F1 is increased by an average of 6%, and the macro-F1 of the Artificial Neural Network (ANN) fusion model is increased by an average of 5.3% compared with LSTM and ANN. Conclusion: The proposed model can effectively improve the accuracy of identification compared with the state-based load identification method.

Background: With the applications of wireless sensor network technology, threedimensional node location technology is crucial. The process of node localization has some disadvantages, such as the uneven distribution of anchor nodes and the high cost of the network. Therefore, the mobile anchor nodes are introduced to effectively solve accurate positioning. Objective: Considering the estimated distance error, the received signal strength indication technology is used to optimize the measurement of the distance. At the same time, dynamic stiffness planning is introduced to increase virtual anchor nodes. The bird swarm algorithm is also used to solve the optimal location problem of nodes. Method: Firstly, the dynamic path is introduced to increase the number of virtual anchor nodes. At the same time, the improved RSSI distance measurement technology is introduced to the node localization. Then, an intelligent three-dimensional node localization algorithm based on dynamic path planning is proposed. Finally, the proposed algorithm is compared with similar algorithms through simulation experiments. Results: Simulation results show that the node coordinates obtained by the proposed algorithm are more accurate, and the node positioning accuracy is improved. The execution time and network coverage of the algorithm are better than similar algorithms. Conclusion: The proposed algorithm significantly improves the accuracy of node positioning. However, the traffic of the algorithm is increased. A little increase in traffic in exchange for positioning accuracy is worthy of recognition. The simulation results show that the proposed algorithm is robust and can be implemented and promoted in the future.

Background: In the contemporary world, the use of energy, especially electrical energy, is increasing rapidly. Without this power, modern civilized life cannot think for a moment. Thereby a huge amount of electrical energy is needed. We have to generate a considerable amount of electricity to meet the growing demand maintaining necessary conditions and constraints. A rapid divergent strategy called Economic Load Dispatch (ELD) has been introduced to distribute generated energy economically during times of crisis. The problem of economic load dispatch is solved with the help of Optimal Power Flow (OPF) formation. The OPF is a primary tool related to the optimum generation schedule available in an interconnected power system to reduce production costs subject to the limitations and constraints of the relevant system. The static economic dispatch optimizes over single dispatch intervals, which is called “ED static”. Due to high load variability and uncertainty, the ED-static is not working properly. Thereby a deterministic look-ahead dispatch is established, which takes care of the increasing renewable penetration. To take decisions against uncertainty, stochastic load dispatch is required. In this research paper, an economic study of the modified IEEE 5 bus power system will be presented. Moreover, some intelligent changes to this system will include the goal of reducing costs, including maximum power supply. The optimal power flow of the modified IEEE 5 bus system considering plausible scenario will represent based on stochastic load dispatch optimization model. Methods: The extension version of Economic load dispatch where load flow equations are applied, and as a system of supply-demand balance constraints is called optimal power flow. To solve the optimal power flow Lagrangian method is used. Karusk-Khun-Tucker (KKT) conditions are applied to solving the Lagrangian method. Results: The results show that during uncertainty and stochastic loads, the storage system with renewable power and generator is suitable for cost reduction with maximum supply. Conclusion: The results further show that for any type of loads, such as fixed load and stochastic load in IEEE 5Bus system, if both renewable energies, such as wind power, solar power, the storage devices are used in the generation system, which ensures maximum power supply with the cost is reduced.